Explore solutions by selecting your industry
- Real Estate
- Retail
- Semiconductors
- Technology, Media & Telecommunications
- Travel, Logistics & Infrastructure
Industries
❮
- Advanced Electronics
- Aerospace & Defense
- Agriculture
- Chemicals
- Consumer Packaged Goods
- Electronic Power & Natural Gas
- Engineering, Construction & Building Materials
- Financial Services
- Metals & Mining
- Oil & Gas
- Paper, Forest Products & Packaging
- Pharmaceuticals & Medical Products
- Private Equity & Principal Investor
- Public & Social Sector
- Real Estate
- Retail
- Semiconductoros
- Technology, Media & Telecommunications
- Travel, Logistics & Infrastructure
What it is
FrontlineAI’s complete suite of workforce management solutions combines the latest AI technologies with best-in-class planning and coaching processes
Solutions
Long-range planning
(6+ months ahead)
Develop and compare scenarios to build a long-term workforce strategy, using AI powered insights for demand and capacity
Mid-range planning
(2-6 months ahead)
Leverage AI forecasts to conduct efficient workforce planning over a mid-range time horizon
Capacity planning
(1-4 weeks ahead)
An AI-driven solution for efficient workforce allocation to optimally meet short-term demand
Day of job
A data-driven and user-centric experience that empowers Dispatchers to truly own their work
Employee coach
(0-7 days after)
One-stop center for employees, supervisors and managers to interact with each other and manage performance
Forecast
Frontline.ai's customizable forecast engine delivers best-in-class accuracy across variables over time at a granular level with actionable, detailed insights and a feedback loop to drive improvement and combat natural model decay. It can be fully integrated into your existing tech stack and systems
Time-series training pipeline structure
Data cleaning
Automated data pre-processing designed for time-series data. This step includes automated outlier identification and replacement as well as machine learning driven smoothing
Summarizing time-series features and components
Decomposing the time-series into trend and seasonality and calculating different metrics to quantify the level of noise and irregularities across multiple dimensions
Automated model selection
Shortlist the range of models to be applied to every time-series based on the time-series metrics. This will enable a more targeted modelling approach, leading to higher accuracy and lower run-time
Model validation and hyper-parameter tuning
Leverages Bayesian-optimization to select the best set of hyperparameters for every model by leveraging multiple validation approaches. We use ensemble methods to ensure that the strength of every model is incorporated to increase the overall performance
Post-processing and bias adjustment
Automated adjustment of one-off events (e.g., Black Friday) that cannot be captured through the machine learning models
Schedule
Scheduling technology comprises geographic & non-geographic AI engines that utilize accurate AI forecasts of supply & demand, to optimize shift schedules & geographic job routing to meet any given business objective (e.g. cost). Technology is fully flexible to incorporate business constraints and optimization targets
Geographic optimization
This AI engine leverages linear programming to optimize schedules for jobs at varying locations for:
Working hours
Constrains schedules to the available FTE & overtime hours at a site level
No overlap between jobs
Ensures each job is completed within its target completion window before next job is allocated
Job priority
Flexible to prioritize certain jobs above others based on importance to client
Client specific job constraints
Ability to prioritize completion of jobs based on client required constraints (e.g. those that are ongoing with a third-party, within their completion window, already started or closest to due date)
Idle tim and travel time
Minimizes total idle time of all employees by optimally allocating jobs and loaning employees around different work centers
Schedule creation follows a three-step optimization process
Job Prioritization
We prioritize certain work orders, e.g:
- Ongoing with third parties (customer, service, etc.)
- Started a previous day
- In completion window
- Closest due date
Job to team matching
This is carried out via three methodologies:
- Scoring: Uses scoring functions to determine the optimal crew for the job
- Cluster: Shuffling techs across offices within the same cluster after meeting tech’s home office’s demand
- Overtime: Accommodating extra work hours for techs to complete jobs for a day
Intraday Shuffling
Lastly, our engine leverages mixed integer programming to minimize idle time by optimizing schedules for:
- Travel time
- Idle time
- While still verifying constraints of coordinated times and precedence
Input
Engine
Output
Demand inputs
- Call volume, average handle time, and shrinkage forecast
- Mapping of demand to role types
Our extremely high accuracy AI-generated forecast enables schedule optimization at a much more granular level
Supply Constraints
- Call volume, average handle time, and shrinkage forecast
- Mapping of demand to role types
Our extremely high accuracy AI-generated forecast enables schedule optimization at a much more granular level
Schedule optimizer
- Linear programming based solver
- Allocates supply to demand while minimizing scheduling cost
- Our extremely high accuracy AI-generated forecast enables schedule optimization at a much more granular level
Optimized Schedule Output
- Results of optimized schedules can be compared across multiple scenarios to assess best course of action
- Visual dashboards of optimized output enable user to make critical operational decisions on scheduling, workforce allocation, and further test alternative scenario
Coach
An AI-powered solution that enables organizations to improve the performance of front-line employees through dashboards, personalized nudges, advice on training and peer-recognition
Our technology determines the next best action for supervisors to take, and sends nudges directly to employees
SAIC architecture (simplified)
TEAM HUDDLE
Local Manager
AI coach proposes topics to bring up during team huddles based on topics relevant to multiple employees on the team
ACTION NUDGE
Employee
AI coach sends nudges about certain metrics (e.g., time at garage and driving time)
COACHING
Local Manager
Supervisor receives information on most impactful area of improvement for employees (e.g., misdiagnosis of issues, avoidable equipment swaps), which they can use to better coach them
ACTION NUDGE
Employee
Employee receives a nudge before a job, reminding them to perform compliance checks along the way
BEST PRACTICES NUDGE
Employee
AI coach can send best practices ahead of time to help an employee complete tasks that have historically been challenging (e.g., connecting customers at the hub)
COACHNG
Local Manager
Supervisors receive a prioritized summary of key areas of improvement for employees on their team, (e.g., how to install a set-top box)
Technology
Proprietary technologies
Forecast
AI-powered forecasting across demand and supply variables for monthly, daily, and interval time horizons
Simulate
Digital twin simulations that model the workforce to create highly flexible and accurate scenario analyses
Schedule
AI-enabled engine that optimally matches supply to demand at any time interval with full flexibility in business constraints and optimization targets
Coach
Next best action recommendation engine that integrates actionable nudges into digital interfaces for both employees and supervisors
Enabling technologies
Intuitive UI
Graphical visualizations for both managers and the frontline to access insights and interact with underlying AI models
Automated cloud infrastructure
Proprietary data pipelines that enable rapid deployment & maintenance as well as seamless data syncing
Forecast
Frontline.ai's customizable forecast engine delivers best-in-class accuracy across variables over time at a granular level with actionable, detailed insights and a feedback loop to drive improvement and combat natural model decay. It can be fully integrated into your existing tech stack and systems
Time-series training pipeline structure
Data cleaning
Automated data pre-processing designed for time-series data. This step includes automated outlier identification and replacement as well as machine learning driven smoothing
Summarizing time-series features and components
Decomposing the time-series into trend and seasonality and calculating different metrics to quantify the level of noise and irregularities across multiple dimensions
Automated model selection
Shortlist the range of models to be applied to every time-series based on the time-series metrics. This will enable a more targeted modelling approach, leading to higher accuracy and lower run-time
Model validation and hyper-parameter tuning
Leverages Bayesian-optimization to select the best set of hyperparameters for every model by leveraging multiple validation approaches. We use ensemble methods to ensure that the strength of every model is incorporated to increase the overall performance
Post-processing and bias adjustment
Automated adjustment of one-off events (e.g., Black Friday) that cannot be captured through the machine learning models
Schedule
Scheduling technology comprises geographic & non-geographic AI engines that utilize accurate AI forecasts of supply & demand, to optimize shift schedules & geographic job routing to meet any given business objective (e.g. cost). Technology is fully flexible to incorporate business constraints and optimization targets
Geographic optimization
This AI engine leverages linear programming to optimize schedules for jobs at varying locations for:
Working hours
Constrains schedules to the available FTE & overtime hours at a site level
No overlap between jobs
Ensures each job is completed within its target completion window before next job is allocated
Job priority
Flexible to prioritize certain jobs above others based on importance to client
Client specific job constraints
Ability to prioritize completion of jobs based on client required constraints (e.g. those that are ongoing with a third-party, within their completion window, already started or closest to due date)
Idle tim and travel time
Minimizes total idle time of all employees by optimally allocating jobs and loaning employees around different work centers
Schedule creation follows a three-step optimization process
Job Prioritization
We prioritize certain work orders, e.g:
- Ongoing with third parties (customer, service, etc.)
- Started a previous day
- In completion window
- Closest due date
Job to team matching
This is carried out via three methodologies:
- Scoring: Uses scoring functions to determine the optimal crew for the job
- Cluster: Shuffling techs across offices within the same cluster after meeting tech’s home office’s demand
- Overtime: Accommodating extra work hours for techs to complete jobs for a day
Intraday Shuffling
Lastly, our engine leverages mixed integer programming to minimize idle time by optimizing schedules for:
- Travel time
- Idle time
- While still verifying constraints of coordinated times and precedence
Input
Engine
Output
Demand inputs
- Call volume, average handle time, and shrinkage forecast
- Mapping of demand to role types
Our extremely high accuracy AI-generated forecast enables schedule optimization at a much more granular level
Supply Constraints
- Call volume, average handle time, and shrinkage forecast
- Mapping of demand to role types
Our extremely high accuracy AI-generated forecast enables schedule optimization at a much more granular level
Schedule optimizer
- Linear programming based solver
- Allocates supply to demand while minimizing scheduling cost
- Our extremely high accuracy AI-generated forecast enables schedule optimization at a much more granular level
Optimized Schedule Output
- Results of optimized schedules can be compared across multiple scenarios to assess best course of action
- Visual dashboards of optimized output enable user to make critical operational decisions on scheduling, workforce allocation, and further test alternative scenario
Coach
An AI-powered solution that enables organizations to improve the performance of front-line employees through dashboards, personalized nudges, advice on training and peer-recognition
Our technology determines the next best action for supervisors to take, and sends nudges directly to employees
SAIC architecture (simplified)
TEAM HUDDLE
Local Manager
AI coach proposes topics to bring up during team huddles based on topics relevant to multiple employees on the team
ACTION NUDGE
Employee
AI coach sends nudges about certain metrics (e.g., time at garage and driving time)
COACHING
Local Manager
Supervisor receives information on most impactful area of improvement for employees (e.g., misdiagnosis of issues, avoidable equipment swaps), which they can use to better coach them
ACTION NUDGE
Employee
Employee receives a nudge before a job, reminding them to perform compliance checks along the way
BEST PRACTICES NUDGE
Employee
AI coach can send best practices ahead of time to help an employee complete tasks that have historically been challenging (e.g., connecting customers at the hub)
COACHNG
Local Manager
Supervisors receive a prioritized summary of key areas of improvement for employees on their team, (e.g., how to install a set-top box)
Achieving significant impact in less than 6 months, across industries
Impact
20-30%
cost saving
10-20%
improved customer experience
5-10%
improvement in employee engagement
Levers
Increased employee productivity
10-15% increase in intra-day employee utilization
10-20% increase in on-the-job effectiveness
Enhanced customer experience (NPS)
20-30% decrease in customer wait time
5-10% increase in on-time responses to customers
Higher employee satisfaction
Improvement in pulse survey scores
Significantly reduced employee attrition
How it works
An initial fact-base and view of high-impact opportunities can be delivered through a 4-week 'proof-of-value' sprint
Typical approach for deploying Trace solution
Setup
Remote install solution on machines
Execute
Execute processes by SMEs and review outpus
Run trace
Run analysis with data and build insights
Prioritize
Review results, and prioritize improvements interventions
What it looks like
Baseline
- Data ingestion from all major ERPs, AI-driven supplier-harmonization and spend classification
- Baseline on company, asset & product-level using supplier location and materials spec
- Engine with 18,000+ emission factors, 70+ sector benchmarks
Click thumbnails to expand
Value
- Automatic selection from 5,000+ levers based on baseline
- Rapid establishment of abatement curves and pathways
- Interactively view changes in MACC based on selected levers
Click thumbnails to expand
Plan
- Cost & impact business cases to refine levers into initiatives and inform sequencing
- 1,000+ product cleansheets to create tailored initiatives
- Expert-led support for new initiatives design
Click thumbnails to expand
Act
- Proven technology for transformation management, in operation for 10+ years and with 155k active users
- Automated reporting providing full transparency and single-source of truth
- Feedback loop to reconcile progress made against target
Click thumbnails to expand
Here are some client examples
Telecom field force achieved 10-20% capacity generation while improving NPS by 15-20 bp by deploying supervisor dashboard, smart AI coach, and forecasting & scheduling
Click thumbnail to expand
Large call center improved NPS 10-15% and reduced costs 4-7% by deploying AI Forecasting Engine for 50k+ employees
Click thumbnail to expand
Retailer improved forecasting & scheduling to achieve 10-20% cost savings, 10-20% sales increase, and 5-10% utilization increase
Click thumbnail to expand
By working with us, you will have access to
Experts
Our industry and functional experts will be on the ground where and when you need them.
Helpdesk
Our helpdesk will keep doors open and commit to addressing your concerns efficiently and effectively.
Training
We offer coaching and build capabilities for your teams to deliver sustained improvements.
Implementation
We partner with you and co-create a firm path to implementation as part of the project.
